Covid-19 pandemi sürecinde uzaktan eğitimin twitter yansımalarının duygu analizi

Year 2022, Volume: 8 Issue: 3, 228 - 242, 31.07.2022

Engin Kandıran , Burcu Gumus , Mehmet Ali Özer

https://doi.org/10.24289/ijsser.1102248

Cited By: 3

Abstract

Çalışmanın amacı Covid-19 pandemi sürecinde uzaktan eğitime geçilmesinin Türk toplumu tarafından nasıl değerlendirildiğini sosyal medya paylaşımları üzerinden duygu (sentiment) analizi yapılarak anlamaktır. Bu amaçla örgün eğitime ara verilen tarih olan 16 Mart 2020 ile 17 Mayıs 2021 arasında Twitter’da eğitim ile ilgili öne çıkan 28 etiket belirlenmiştir. Twitter API aracılığıyla sadece Türkçe 8545 tweet elde edilerek veri seti oluşturulmuştur. Ayrıca ilgili dönemde yetkililer tarafından günlük aktarılan vaka sayılarının paylaşımları olumlu mu olumsuz mu etkilediği de değerlendirilmiştir. Son olarak veri setinin içinde en çok tekrar eden kelimeler değerlendirilmiştir. Böylece en çok tekrar eden açıklamaların neler olduğu belirlenmiştir. Analizler sonucunda, uzaktan eğitime ilişkin tweet’lerin vaka sayılarındaki artış ile paralellik gösterdiği belirlenmiştir. Ayrıca, paylaşımda bulunan kişilerin genelde sağlığa dayalı endişelerden ötürü uzaktan eğitim ilgili pozitif paylaşımlarda bulunulduğu belirlenmiştir.

Keywords

Uzaktan eğitim, Sentiment analiz, Twitter, Covid-19, BERT modeli

Sentiment analysis of twitter reflections of distance education in the covid-19 pandemic process

Abstract

The aim of the study is to understand how the Turkish society evaluates the transition to distance education during the Covid-19 pandemic process by making sentiment analysis over Twitter posts. Hence, 28 prominent education-related tags were determined between 16.03.2020 - 17.05.2021. The data set was created by obtaining 8545 tweets in Turkish via the Twitter API. In addition, it was evaluated whether the number of cases reported daily by the authorities in the relevant period affected the shares positively or negatively. Finally, the most repeated words were evaluated to establish the most repetitive explanations. As a result, it was determined that the tweets related to distance education were in parallel with the increase in the number of cases and positive sharing due to health-related concerns.

Keywords

Distance education, Sentiment analysis, Twitter, Covid-19, BERT model

References

• Aksoğan, M., & Duman, Ç.M., (2020). A research on academician opinions on distance education in the COVID-19 Process.NATURENGS, MTU Journal of Engineering and Natural Sciences, Special Issue,38-49. doı: 10.46572/nat.2020.10
• Arat, T., ve Bakan, Ö. (2011). Uzaktan eğitim ve uygulamaları. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksek Okulu Der¬gisi, 365, 14(1-2).
• Basalaia, G., ve Kvavadze, D. (2020). Transition to online education in schools during a SARS-Cov-2 Coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research, 5(4), 1-9.
• Bozkurt, A.,ve Sharma, R. C. (2020). Emergency remote teaching in a time of global crisis due to Corona virus pandemic. Asian Journal of Distance Education, 15(1), i-vi. doi:10.5281/zenodo.3778083
• Carter, R. (2004). Language and creativity; the art of common talk. London: Routledge.
• Corcuera, L.C., ve Alvarez, A.V. (2021). Teacher's roadblocks in the time of quarantine teaching. International Journal of Social Sciences and Education Research, 7 (4), 427-434. doı: https://doi.org/10.24289/ijsser.1003162
• D’Agostino M. (2020). Analysis of social media data about COVID-19 in the Americas, WHO. (2020) https://www.who.int/docs/default-source/epi-win/presentations-of-all-speeches/webinar-18-sgs-ib-8-april-2020.pdf?sfvrsn=db304bde_2
• Danjou, P. E. (2020). Distance teaching of organic chemistry tutorials during the COVID-19 pandemic: Focus on the Use of videos and social media. Journal of Chemical Education, A-D. doi:10.1021/acs.jchemed.0c00485
• Demirtas, E., ve Pechenizkiy, M. (2013). Cross-Lingual polarity detection with machine translation. In proceedings of the second ınternational workshop on ıssues of sentiment discovery and opinion mining (WISDOM ’13)
• Devlin, J., Chang, M.W., Lee K., ve Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding https://huggingface.co/savasy/bert-base-turkish-sentiment-cased
• Gottlieb, M., ve Dyer, S. (2020). Information and disinformation: social media in the COVID-19 crisis. Academic Emergency Medicine, 27, 640-641.
• Gupta, A. ve Coven, J. (2020) Disparities in mobility responses to COVID-19 https://static1.squarespace.com/static/56086d00e4b0fb7874bc2d42/t/5ebf201183c6f016ca3abd91/1589583893816/DemographicCovid.pdf
• Guragai, M. (2020). Nepalese medical students in the COVID-19 pandemic: Ways forward, Journal of the Nepal Medical Association, 58(225), 352-354.
• Hayran, A., & Sert, M. (2017). Sentiment analysis on microblog data based on word embedding and fusion techniques, IEEE 25th Signal Processing and Communications Applications Conference (SIU 2017), Belek, Turkey
• Hermida, A., Fletcher, F., Korell, D., ve Logan, D. (2012). Share, like, recommend. Journalism Studies, 13, 815-824.
• Ho, J., ve Tay, L. Y. (2020). Ensuring learning continues during a pandemic. International Studies in Educational Administration, 48, 49-55.
• Kamps, J. Marx, M., Mokken, R. J. ve De Rijke, M. (2004). Using wordnet to measure semantic orientations of adjectives, proceedings of the fourth ınternational conference on language resources and evaluation (LREC’04), Lisbon, Portugal 1115 - 1188 http://www.lrec-conf.org/proceedings/lrec2004/pdf/734.pdf Kar, A. K., ve Dwivedi, Y. K. (2020). Theory building with big data-driven research –moving away from the “what” towards the “why”. International Journal of Information Management, 54, 1–10. 10.1016/j.ijinfomgt.2020.102205
• Kharde, V.A. & Sonawane, S. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications (0975 – 8887), 139(11), 5 – 15
• Kırık, A. M. (2014). Uzaktan eğitimin tarihsel gelişimi ve Türkiye’deki durumu. Marmara İletişim Dergisi, 73-94, 21.
• Koloğlu, T. F., Kantar, M., ve Doğan, M. (2016). Öğretim elemanlarının uzaktan eğitimde hazır bulunurluklarının önemi, AUAd, 2(1): 52-70.
• Millî Eğitim Bakanlığı. (2020a). Bakan Selçuk, koronavirüs’e karşı eğitim alanında alınan tedbirleri açıkladı. https://www.meb.gov.tr/bakan-selcuk-koronaviruse-karsi-egitimalaninda-alinan-tedbirleri-acikladi/haber/20497/tr
• Mishra, L., Gupta, T., ve Shree, A. (2020). Online teaching-learning in higher education during lockdown period of Covid-19 pandemic. International Journal of Educational Research Open, 1.
• Prensky, M. (2007). How to teach with technology: Keeping both teachers and students comfortable in an era of exponential change. Emerging Technologies for Learning, 2, 40-46.
• Sözen, N. (2020). COVID 19 sürecinde uzaktan eğitim uygulamaları üzerine bir inceleme. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi (ASEAD), 7(12), 302-319.
• Taboada, M., Brooke, J., Tofiloski, M., Voll, K.,ve Stede, M. (2011). Lexicon based methods for sentiment analysis. Computational Linguistics, 37(2), 267-307.
• Toquero, C. M. (2020). Emergency remote teaching amid COVID-19: The turning point. Asian Journal of Distance Education,15(1), 185-188. doi:10.5281/zenodo.3881748
• Ofcom. 2008. Social networking: A quantitative and qualitative research report into attitudes, behaviours and use. Office of Communications: London. Özdoğan, A. Ç., ve Berkant, H. G. (2020). COVID-19 pandemi dönemindeki uzaktan eğitime ilişkin paydaş görüşlerinin incelenmesi. Millî Eğitim, 49(1), 13-43.
• Valentine, D. (2002). Distance learning: Promises, problems, and possibilities. Online Journal of Distance Learning Administration, 5(3). 1-11 https://www.westga.edu/~distance/ojdla/fall53/valantine53.pdf
• Warschauer, M. (2004). Technology and social inclusion: Rethinking The digital divide. Cambridge, Massachusetts: The MIT Press.
• WHO (2020). Responding to community spread of COVID-19 : Interim guidance, https://www.who.int/docs/defaultsource/coronaviruse/20200307-responding-to-COVID-19 -communitytransmission-final.pdf.
• WHO (2021a). WHO Coronavirus (COVID-19) Dashboard. https:// covid19.who.int/.
• WHO. (2021b). COVID-19 Global excess mortality. Retrieved from https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality
• Yamamoto Telli G., ve Deniz, A. (2020). Coronavirüs ve çevrimiçi (Online) eğitimin önlenemeyen yükselişi. Journal of University Research 3(1), 25-34.
• Yip, P. S. F., ve Chau, P.H. (2020). Physical distancing and emotional closeness amidst COVID-19. Crisis, 41(3), 153-155.
• YÖK. (2020a). Üniversitelerde uygulanacak uzaktan eğitime ilişkin açıklama. https://www.yok.gov.tr/Sayfalar/Haberler/2020/